The stock market currently remains one of the most difficult systems to model in finance. Hence, it is a challenge to solve stock portfolio allocation wherein an optimal investment strategy must be found for a curated collection of stocks that effectively maximizes return while minimizing the risk involved. Deep reinforcement learning approaches have shown promising results when used to automate portfolio allocation, by training an intelligent agent on historical stock prices. However, modern investors are actively engaging with digital platforms such as social media and online news websites to understand and better analyze portfolios. The overall attitude thus formed by investors toward a particular stock or financial market is known as market sentiment. Existing approaches do not incorporate market sentiment which has been empirically shown to influence investor decisions. In our paper, we propose a novel deep reinforcement learning approach to effectively train an intelligent automated trader, that not only uses the historical stock price data but also perceives market sentiment for a stock portfolio consisting of the Dow Jones companies. We demonstrate that our approach is more robust in comparison to existing baselines across standardized metrics such as the Sharpe ratio and annualized investment return.

股票市场目前仍然是最难建模的金融系统之一。因此，解决股票投资组合分配的挑战是一项挑战，其中必须找到一种最佳的投资策略来精选精选的股票，以有效地使收益最大化，同时将所涉及的风险降至最低。深度强化学习方法通​​过对历史股价进行智能代理培训，在用于自动分配投资组合时显示出令人鼓舞的结果。但是，现代投资者正在积极参与社交媒体和在线新闻网站等数字平台，以了解和更好地分析投资组合。投资者对特定股票或金融市场形成的总体态度被称为市场情绪。现有方法并未纳入市场情绪，而市场情绪已通过经验证明会影响投资者的决策。在本文中，我们提出了一种新颖的深度强化学习方法，可以有效地训练智能自动交易员，该方法不仅使用历史股票价格数据，而且可以感知由道琼斯公司组成的股票投资组合的市场情绪。我们证明，与现有标准相比，与夏普比率和年度投资回报率等标准化指标相比，我们的方法更为稳健。它不仅使用历史股价数据，而且可以感知由道琼斯公司组成的股票投资组合的市场情绪。我们证明，与现有标准相比，与夏普比率和年度投资回报率等标准化指标相比，我们的方法更为稳健。它不仅使用历史股价数据，而且可以感知由道琼斯公司组成的股票投资组合的市场情绪。我们证明，与现有标准相比，与夏普比率和年度投资回报率等标准化指标相比，我们的方法更为稳健。